From e836fcb9d8658470f5ca5a3f2dd28068faa0c40f Mon Sep 17 00:00:00 2001 From: Kahsolt Date: Tue, 3 Jan 2023 12:27:11 +0800 Subject: [PATCH] fixup #1: AttributeError --- README.md | 26 ++++++++++++++--- scripts/sonar.py | 76 ++++++++++++++++++++++++++++-------------------- 2 files changed, 66 insertions(+), 36 deletions(-) diff --git a/README.md b/README.md index ae7b2be..65da0d5 100644 --- a/README.md +++ b/README.md @@ -1,9 +1,19 @@ # stable-diffusion-webui-sonar - Wrapped k-diffuison samplers with tricks to improve the generated image quality, extension script for AUTOMATIC1111/stable-diffusion-webui + Wrapped k-diffuison samplers with tricks to improve the generated image quality (maybe?), extension script for AUTOMATIC1111/stable-diffusion-webui ---- +

+ Last Commit + GitHub issues + GitHub stars + GitHub forks + Language + License +
+

+ ℹ This is the sister repo of [https://github.com/Kahsolt/stable-diffusion-webui-prompt-travel](https://github.com/Kahsolt/stable-diffusion-webui-prompt-travel), it focuses on **single prompt optimization** rather than traveling between multiple prompts. The core idea of Sonar is to search for similar (yet even better!) images in the **neighborhood** of some known image generated by a normal denoising process. @@ -21,10 +31,18 @@ to get image latents with higher quality (~perhaps!), and just pray again for go ### Change Log +⚪ Features + - 2022/11/27: add momentum on `Euler`, add hard ref-image guidance on `Naive` - 2022/11/20: add an Euler-like `Naive`, the simplest difference-estimation-based sampler with momentum & gradient - 2022/11/18: add momentum on `Euler a` +⚪ Fixups + +- 2023/01/03: fix issue #1 with webui's updates (error `AttributeError: 'StableDiffusionProcessingTxt2Img' object has no attribute 'firstphase_height'`) + + +### Examples ⚪ momentum @@ -102,12 +120,12 @@ This repo allows your to quickly implement your own k-diffusion samplers, follow Easiest way to install it is to: 1. Go to the "Extensions" tab in the webui, switch to the "Install from URL" tab -2. Paste https://github.com/Kahsolt/stable-diffusion-webui-sonar.git into "URL for extension's git repository", click "install" button -3. Go to the "Installed" tab, click "Apply and Restart UI" button +2. Paste https://github.com/Kahsolt/stable-diffusion-webui-sonar.git into "URL for extension's git repository" and click install +3. (Optional) You will need to restart the webui for dependencies to be installed or you won't be able to generate video files Manual install: 1. Copy this repo folder to the 'extensions' folder of https://github.com/AUTOMATIC1111/stable-diffusion-webui -2. Go to the "Settings" tab, click "Restart Gradio and Refresh components" button +2. (Optional) Restart the webui ---- diff --git a/scripts/sonar.py b/scripts/sonar.py index bcc37e4..bce894a 100644 --- a/scripts/sonar.py +++ b/scripts/sonar.py @@ -5,6 +5,7 @@ import gradio as gr import torch +from torch import Tensor import numpy as np from tqdm.auto import trange @@ -28,16 +29,16 @@ DEFAULT_GRAD_X_ALPHA = -0.02 DEFAULT_GRAD_FUZZY = False DEFAULT_REF_METH = 'linear' -DEFAULT_REF_HGF = 0.02 +DEFAULT_REF_HGF = 0.01 DEFAULT_REF_MIN_STEP = 0.0 -DEFAULT_REF_MAX_STEP = 0.5 +DEFAULT_REF_MAX_STEP = 0.75 DEFAULT_REF_IMG = None CHOICE_MOMENTUM_SIGN = ['pos', 'neg', 'rand'] CHOICE_MOMENTUM_HIST_INIT = ['zero', 'rand_init', 'rand_new'] CHOICE_REF_METH = ['linear', 'euler'] -# debug save latent featmap (when `euler a`) +# debug save latent featmap (when `Euler a`) #FEAT_MAP_PATH = 'C:\sd-webui_featmaps' FEAT_MAP_PATH = None @@ -64,8 +65,6 @@ 'ref_img': DEFAULT_REF_IMG, } -Tensor = torch.Tensor - # ↓↓↓ the following is modified from 'modules/processing.py' ↓↓↓ def process_images(p: StableDiffusionProcessing) -> Processed: @@ -73,13 +72,21 @@ def process_images(p: StableDiffusionProcessing) -> Processed: try: for k, v in p.override_settings.items(): - setattr(opts, k, v) # we don't call onchange for simplicity which makes changing model, hypernet impossible + setattr(opts, k, v) + if k == 'sd_hypernetwork': shared.reload_hypernetworks() # make onchange call for changing hypernet + if k == 'sd_model_checkpoint': sd_models.reload_model_weights() # make onchange call for changing SD model + if k == 'sd_vae': sd_vae.reload_vae_weights() # make onchange call for changing VAE res = process_images_inner(p) finally: - for k, v in stored_opts.items(): - setattr(opts, k, v) + # restore opts to original state + if p.override_settings_restore_afterwards: + for k, v in stored_opts.items(): + setattr(opts, k, v) + if k == 'sd_hypernetwork': shared.reload_hypernetworks() + if k == 'sd_model_checkpoint': sd_models.reload_model_weights() + if k == 'sd_vae': sd_vae.reload_vae_weights() return res @@ -179,10 +186,9 @@ def infotext(iteration=0, position_in_batch=0): else: raise ValueError samples_ddim = sample_func(p, conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts) - samples_ddim = samples_ddim.to(devices.dtype_vae) - x_samples_ddim = decode_first_stage(p.sd_model, samples_ddim) + x_samples_ddim = [decode_first_stage(p.sd_model, samples_ddim[i:i+1].to(dtype=devices.dtype_vae))[0].cpu() for i in range(samples_ddim.size(0))] + x_samples_ddim = torch.stack(x_samples_ddim).float() x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0) - del samples_ddim if shared.cmd_opts.lowvram or shared.cmd_opts.medvram: @@ -263,18 +269,22 @@ def StableDiffusionProcessingTxt2Img_sample(self:StableDiffusionProcessingTxt2Im # hijack the sampler~ self.sampler = create_sampler(self.sd_model) + latent_scale_mode = shared.latent_upscale_modes.get(self.hr_upscaler, None) if self.hr_upscaler is not None else shared.latent_upscale_default_mode + if self.enable_hr and latent_scale_mode is None: + assert len([x for x in shared.sd_upscalers if x.name == self.hr_upscaler]) > 0, f"could not find upscaler named {self.hr_upscaler}" + + x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) + samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x)) + if not self.enable_hr: - x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) - samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x)) return samples - x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) - samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x, self.firstphase_width, self.firstphase_height)) - - samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2] + target_width = int(self.width * self.hr_scale) + target_height = int(self.height * self.hr_scale) - """saves image before applying hires fix, if enabled in options; takes as an arguyment either an image or batch with latent space images""" def save_intermediate(image, index): + """saves image before applying hires fix, if enabled in options; takes as an argument either an image or batch with latent space images""" + if not opts.save or self.do_not_save_samples or not opts.save_images_before_highres_fix: return @@ -283,13 +293,13 @@ def save_intermediate(image, index): images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, suffix="-before-highres-fix") - if opts.use_scale_latent_for_hires_fix: + if latent_scale_mode is not None: for i in range(samples.shape[0]): save_intermediate(samples, i) - samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear") + samples = torch.nn.functional.interpolate(samples, size=(target_height // opt_f, target_width // opt_f), mode=latent_scale_mode) - # Avoid making the inpainting conditioning unless necessary as + # Avoid making the inpainting conditioning unless necessary as # this does need some extra compute to decode / encode the image again. if getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) < 1.0: image_conditioning = self.img2img_image_conditioning(decode_first_stage(self.sd_model, samples), samples) @@ -307,7 +317,7 @@ def save_intermediate(image, index): save_intermediate(image, i) - image = images.resize_image(0, image, self.width, self.height) + image = images.resize_image(0, image, target_width, target_height, upscaler_name=self.hr_upscaler) image = np.array(image).astype(np.float32) / 255.0 image = np.moveaxis(image, 2, 0) batch_images.append(image) @@ -322,15 +332,14 @@ def save_intermediate(image, index): shared.state.nextjob() - # hijack the sampler~ - self.sampler = create_sampler(self.sd_model) - - noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) + noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, p=self) # GC now before running the next img2img to prevent running out of memory x = None devices.torch_gc() + # hijack the sampler~ + self.sampler = create_sampler(self.sd_model) samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps, image_conditioning=image_conditioning) return samples @@ -338,6 +347,10 @@ def save_intermediate(image, index): def StableDiffusionProcessingImg2Img_sample(self:StableDiffusionProcessingImg2Img, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts): x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) + if self.initial_noise_multiplier != 1.0: + self.extra_generation_params["Noise multiplier"] = self.initial_noise_multiplier + x *= self.initial_noise_multiplier + # hijack the sampler~ self.sampler = create_sampler(self.sd_model) samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning, image_conditioning=self.image_conditioning) @@ -446,8 +459,8 @@ def sample_naive_ex(model:CFGDenoiser, x:Tensor, sigmas:List, extra_args={}, cal with devices.autocast(): latent_ref = sd_model.get_first_stage_encoding(sd_model.encode_first_stage(x_ref)) # [B, C=4, H=64, W=64] - avg_s = latent_ref.mean(dim=[1, 2, 3], keepdim=True) - std_s = latent_ref.std (dim=[1, 2, 3], keepdim=True) + avg_s = latent_ref.mean(dim=[2, 3], keepdim=True) + std_s = latent_ref.std (dim=[2, 3], keepdim=True) ref_img_norm = (latent_ref - avg_s) / std_s # stochastics in gradient optimizing @@ -567,7 +580,6 @@ def sample_euler_ex(model:CFGDenoiser, x:Tensor, sigmas:List, extra_args={}, cal momentum_hist = settings['momentum_hist'] momentum_hist_init = settings['momentum_hist_init'] - # 记录梯度历史的惯性 if momentum_hist_init == 'zero': history_d = 0 elif momentum_hist_init == 'rand_init': history_d = x elif momentum_hist_init == 'rand_new': history_d = torch.randn_like(x) @@ -818,7 +830,7 @@ def sample_img2img(self, p:StableDiffusionProcessing, x:Tensor, noise:Tensor, def get_latent_loss(self:LatentDiffusion, x:Tensor, t:Tensor, c:Tensor, noise:Tensor) -> Tensor: - # 这个t似乎是预训练时ImageNet的分类数，随便往一个类方向去引导, 貌似完全不影响结果 + # 这个t是时间嵌入(time embed)，决定了降噪程度(似乎是逆向sigma调度)，貌似对结果影响不大 t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long() if t is None else t # [B=1] x_start = x @@ -882,11 +894,11 @@ def ui(self, is_img2img): momentum_sign = gr.Radio(label='Momentum sign', value=lambda: DEFAULT_MOMENTUM_SIGN, choices=CHOICE_MOMENTUM_SIGN) momentum_hist_init = gr.Radio(label='Momentum history init', value=lambda: DEFAULT_MOMENTUM_HIST_INIT, choices=CHOICE_MOMENTUM_HIST_INIT) - with gr.Group() as tab_gradient: + with gr.Group(visible=False) as tab_gradient: with gr.Row(): grad_c_iter = gr.Slider(label='Optimize cond step count', value=lambda: DEFAULT_GRAD_C_ITER, minimum=0, maximum=10, step=1) grad_c_alpha = gr.Slider(label='Optimize cond step size', value=lambda: DEFAULT_GRAD_C_ALPHA, minimum=-0.01, maximum=0.01, step=0.001) - grad_c_skip = gr.Slider(label='Skip the first n-steps', value=lambda: DEFAULT_GRAD_C_SKIP, minimum=0, maximum=100, step=1) + grad_c_skip = gr.Slider(label='Skip the first n-steps', value=lambda: DEFAULT_GRAD_C_SKIP, minimum=0, maximum=100, step=1) with gr.Row(): grad_x_iter = gr.Slider(label='Optimize latent step count', value=lambda: DEFAULT_GRAD_X_ITER, minimum=0, maximum=40, step=1) grad_x_alpha = gr.Slider(label='Optimize latent step size', value=lambda: DEFAULT_GRAD_X_ALPHA, minimum=-0.1, maximum=0.1, step=0.01)